{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "from keras.backend import *\n",
    "import datetime\n",
    "from keras.layers import *\n",
    "from keras.optimizers import Adam\n",
    "import re\n",
    "from keras.callbacks import ModelCheckpoint, TensorBoard\n",
    "import keras.models as KM\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import ChipsDataset\n",
    "from model import UNET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "session = tf.Session(config=config)\n",
    "K.set_session(session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_generator(dataset,shuffle=True, batch_size=1):\n",
    "    \"\"\"A generator that returns images and corresponding target class ids,\n",
    "    bounding box deltas, and masks.\n",
    "    shuffle: If True, shuffles the samples before every epoch\n",
    "    \"\"\"\n",
    "    b = 0  # batch item index\n",
    "    image_index = -1\n",
    "    image_ids = np.copy(dataset.image_ids)\n",
    "\n",
    "    # Keras requires a generator to run indefinitely.\n",
    "    while True:\n",
    "#         try:\n",
    "        # Increment index to pick next image. Shuffle if at the start of an epoch.\n",
    "        image_index = (image_index + 1) % len(image_ids)\n",
    "        if shuffle and image_index == 0:\n",
    "            np.random.shuffle(image_ids)\n",
    "\n",
    "        image_id = image_ids[image_index]\n",
    "        image = dataset.load_image(image_id)\n",
    "        gt_masks = dataset.load_mask(image_id)\n",
    "\n",
    "\n",
    "        # Init batch arrays\n",
    "        if b == 0:\n",
    "            batch_images = np.zeros(\n",
    "                (batch_size,) + image.shape, dtype=np.float32)\n",
    "            batch_gt_masks = np.zeros(\n",
    "                (batch_size, ) + gt_masks.shape, dtype=gt_masks.dtype)\n",
    "\n",
    "\n",
    "        batch_images[b] = image.astype(np.float32)\n",
    "        batch_gt_masks[b] = gt_masks\n",
    "        b += 1\n",
    "\n",
    "        # Batch full?\n",
    "        if b >= batch_size:\n",
    "            inputs = batch_images\n",
    "            outputs = batch_gt_masks\n",
    "            yield inputs, outputs\n",
    "\n",
    "            # start a new batch\n",
    "            b = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = h5py.File('/mnt/sh_flex_storage/malu/venv/multilayer/data/merged_data/data8_data.h5', 'r')\n",
    "val_set = h5py.File('/mnt/sh_flex_storage/malu/venv/multilayer/data/merged_data/data8_val.h5', 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_set['input']\n",
    "val_images = val_set['input']\n",
    "train_masks = train_set['output']\n",
    "val_masks = val_set['output']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1979 16205\n"
     ]
    }
   ],
   "source": [
    "del train_set\n",
    "del val_set\n",
    "NUM_TRAIN = train_images.shape[0]  # 224*224\n",
    "NUM_TEST = val_images.shape[0]\n",
    "print(NUM_TEST, NUM_TRAIN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_dataset = ChipsDataset(train_images, train_masks)\n",
    "# train_gene = data_generator(train_dataset, batch_size=32)\n",
    "val_dataset = ChipsDataset(val_images, val_masks)\n",
    "val_gene = data_generator(val_dataset, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 256, 256, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 256, 256, 64) 1792        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 256, 256, 64) 36928       conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 128, 128, 64) 0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 128, 128, 128 73856       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 128, 128, 128 147584      conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 64, 64, 128)  0           conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 64, 64, 256)  295168      max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 64, 64, 256)  590080      conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 32, 32, 256)  0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 32, 32, 512)  1180160     max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 32, 32, 512)  2359808     conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 32, 32, 512)  0           conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 16, 16, 512)  0           dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 16, 16, 1024) 4719616     max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 16, 16, 1024) 9438208     conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 16, 16, 1024) 0           conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 32, 32, 1024) 0           dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 32, 32, 512)  2097664     up_sampling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 32, 32, 1024) 0           dropout_1[0][0]                  \n",
      "                                                                 conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 32, 32, 512)  4719104     concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 32, 32, 512)  2359808     conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, 64, 64, 512)  0           conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 64, 64, 256)  524544      up_sampling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 64, 64, 512)  0           conv2d_6[0][0]                   \n",
      "                                                                 conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 64, 64, 256)  1179904     concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 64, 64, 256)  590080      conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2D)  (None, 128, 128, 256 0           conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 128, 128, 128 131200      up_sampling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 128, 128, 256 0           conv2d_4[0][0]                   \n",
      "                                                                 conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 128, 128, 128 295040      concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 128, 128, 128 147584      conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2D)  (None, 256, 256, 128 0           conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 256, 256, 64) 32832       up_sampling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 256, 256, 128 0           conv2d_2[0][0]                   \n",
      "                                                                 conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 256, 256, 64) 73792       concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 256, 256, 64) 36928       conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 256, 256, 2)  1154        conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 256, 256, 1)  3           conv2d_23[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 31,032,837\n",
      "Trainable params: 31,032,837\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from model import unet\n",
    "model = unet()\n",
    "model_dir = '/mnt/sh_flex_storage/malu/venv/CHIPS_MRCNN/UNet/logs'\n",
    "model.load_weights(model_dir+'/unet_0001.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(model_dir+'/unet_0015.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results = model.predict_generator(val_gene,32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/sh_flex_storage/malu/venv/lib/python3.5/site-packages/skimage/transform/_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
     ]
    }
   ],
   "source": [
    "image_batch = []\n",
    "mask_batch = []\n",
    "for i in range(100):\n",
    "    image = val_dataset.load_image(i)\n",
    "    mask = val_dataset.load_mask(i)\n",
    "    image_batch.append(image)\n",
    "    mask_batch.append(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_batch = np.array(image_batch)\n",
    "predict_mask = model.predict(image_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_mask = predict_mask.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dmY7Ho1V1y8J9n5z6eLqq3jtP1b+qqg5X1Ter6smqeqKqPj6tr9TxeI0+Vup47NSqzrBVnF/JesywdZhfyXrMsD2ZX9092yXJZUn+LsnvJHlDkr9Nct2cNW2z/meSvO2Ctf+Q5I5p+44k/37uOi+o7w+S3JDk8c1qTnJLkv+epJLclOThuevfpI9PJfmzi+x73fRv60CSa6d/c5ftgx6uTnLDtP3mJD+Yal2p4/EafazU8dhh7ys7w1Zxfk11rfwMW4f5NdW28jNsL+bX3Ge0bkxyqrt/1N0/T3JvkmMz17Rbx5LcM23fk+QDM9bya7r7W0mev2D5UjUfS/LF3vDtJG+tX/0bcbO5RB+XcizJvd19vrt/nORUfvlHhGfT3We7+3vT9otJnkpyKCt2PF6jj0vZl8djh9Zthu3r+ZWsxwxbh/mVrMcM24v5NXfQOpTk2YXbp/PaDe43neRvquqRqjo+rR3s7rPT9k+THJyntG25VM2reHw+Np2SvnvhbY9930dVvT3JO5M8nBU+Hhf0kazo8diGVe5lXeZXssKvmQus7OtlHWbYqPk1d9Bade/u7huSvD/J7VX1B4t39sZ5xpX6Wucq1rzgziTvSHJ9krNJPj1vOVtTVW9K8tUkn+juFxbvW6XjcZE+VvJ4vI6s3fxKVrfurPDrZR1m2Mj5NXfQOpPk8MLta6a1ldDdZ6brc0m+no3Th8+9eip0uj43X4VbdqmaV+r4dPdz3f1Kd/8iyefyy9O5+7aPqroiGy/uL3X316bllTseF+tjFY/HDqxsL2s0v5IVfM1caFVfL+sww0bPr7mD1neTHKmqa6vqDUluTXL/zDVtSVW9sare/Op2kj9M8ng26r9t2u22JN+Yp8JtuVTN9yf5yPRNkZuS/GzhdPC+c8F7/R/MxvFINvq4taoOVNW1SY4k+c5e13ehqqokn0/yVHd/ZuGulToel+pj1Y7HDq3kDFuz+ZWs2GvmYlbx9bIOM2xP5tduP7G/20s2voXwg2x8cv8v5q5nG3X/Tja+efC3SZ54tfYkv5nkRJIfJnkoyVVz13pB3V/OxmnQl7Lx3vJHL1VzNr4Z8p+mY/P9JEfnrn+TPv7LVOdj04vh6oX9/2Lq4+kk75+7/qmmd2fjlPpjSR6dLres2vF4jT5W6njsov+Vm2GrOr+mGld+hq3D/JrqWvkZthfzy2+GBwAYZO63DgEA1pagBQAwiKAFADCIoAUAMIigBQAwiKAFADCIoAUAMIigBQAwyD8D1kmXyNPm3BYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dmY7Ho1V1y8J9n5z6eLqq3jtP1b+qqg5X1Ter6smqeqKqPj6tr9TxeI0+Vup47NSqzrBVnF/JesywdZhfyXrMsD2ZX9092yXJZUn+LsnvJHlDkr9Nct2cNW2z/meSvO2Ctf+Q5I5p+44k/37uOi+o7w+S3JDk8c1qTnJLkv+epJLclOThuevfpI9PJfmzi+x73fRv60CSa6d/c5ftgx6uTnLDtP3mJD+Yal2p4/EafazU8dhh7ys7w1Zxfk11rfwMW4f5NdW28jNsL+bX3Ge0bkxyqrt/1N0/T3JvkmMz17Rbx5LcM23fk+QDM9bya7r7W0mev2D5UjUfS/LF3vDtJG+tX/0bcbO5RB+XcizJvd19vrt/nORUfvlHhGfT3We7+3vT9otJnkpyKCt2PF6jj0vZl8djh9Zthu3r+ZWsxwxbh/mVrMcM24v5NXfQOpTk2YXbp/PaDe43neRvquqRqjo+rR3s7rPT9k+THJyntG25VM2reHw+Np2SvnvhbY9930dVvT3JO5M8nBU+Hhf0kazo8diGVe5lXeZXssKvmQus7OtlHWbYqPk1d9Bade/u7huSvD/J7VX1B4t39sZ5xpX6Wucq1rzgziTvSHJ9krNJPj1vOVtTVW9K8tUkn+juFxbvW6XjcZE+VvJ4vI6s3fxKVrfurPDrZR1m2Mj5NXfQOpPk8MLta6a1ldDdZ6brc0m+no3Th8+9eip0uj43X4VbdqmaV+r4dPdz3f1Kd/8iyefyy9O5+7aPqroiGy/uL3X316bllTseF+tjFY/HDqxsL2s0v5IVfM1caFVfL+sww0bPr7mD1neTHKmqa6vqDUluTXL/zDVtSVW9sare/Op2kj9M8ng26r9t2u22JN+Yp8JtuVTN9yf5yPRNkZuS/GzhdPC+c8F7/R/MxvFINvq4taoOVNW1SY4k+c5e13ehqqokn0/yVHd/ZuGulToel+pj1Y7HDq3kDFuz+ZWs2GvmYlbx9bIOM2xP5tduP7G/20s2voXwg2x8cv8v5q5nG3X/Tja+efC3SZ54tfYkv5nkRJIfJnkoyVVz13pB3V/OxmnQl7Lx3vJHL1VzNr4Z8p+mY/P9JEfnrn+TPv7LVOdj04vh6oX9/2Lq4+kk75+7/qmmd2fjlPpjSR6dLres2vF4jT5W6njsov+Vm2GrOr+mGld+hq3D/JrqWvkZthfzy2+GBwAYZO63DgEA1pagBQAwiKAFADCIoAUAMIigBQAwiKAFADCIoAUAMIigBQAwyD8D1kmXyNPm3BYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAEgCAYAAABsCt3QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEftJREFUeJzt3V3IpGd5B/D/1SSu1A9Marukm6Wmsj1IDxrDEgOVYgmtmpPVE4kHNRRhexBBoT2I9kAP21ItCG0gYjAWaxr8wByktckiSA+MbmSN+Wh0q5HsumbbRkywdE3i1YP3iU7W3bxfc7/zzuzvB8M8c88zM9fNs3Px33uembe6OwAAzN+vLLoAAIBVJWgBAAwiaAEADCJoAQAMImgBAAwiaAEADDIsaFXVW6vqsao6XlW3jHodgHnTv4B5qRG/o1VVFyX5dpI/SnIiydeTvKu7H5n7iwHMkf4FzNOoFa1rkxzv7u9290+T3Jnk0KDXApgn/QuYm4sHPe++JE/M3D6R5I3n2/lltadfnlcMKgXYjZ7Jj/67u3990XWcw6b6V6KHwYXm//KT/LTP1Eb2HRW01lVVh5McTpKX51fzxrp+UaUAC3Bff/b7i65hO/QwuHDd30c2vO+ojw5PJtk/c/uKaeznuvu27j7Y3QcvyZ5BZQBs2rr9K9HDgI0ZFbS+nuRAVV1ZVS9LcmOSuwe9FsA86V/A3Az56LC7n6uq9yb5UpKLktze3Q+PeC2AedK/gHkado5Wd9+T5J5Rzw8wiv4FzItfhgcAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAYRNACABhE0AIAGETQAgAY5OLtPLiqHk/yTJLnkzzX3Qer6rIk/5zkdUkeT/LO7v7R9soEmD89DBhtHitaf9jdV3f3wen2LUmOdPeBJEem2wC7lR4GDDPio8NDSe6Ytu9I8vYBrwEwih4GzM12g1Yn+beqeqCqDk9je7v71LT9wyR7z/XAqjpcVUer6uizObPNMgC2RA8DhtrWOVpJ3tTdJ6vqN5LcW1X/MXtnd3dV9bke2N23JbktSV5dl51zH4DB9DBgqG2taHX3yen6dJIvJLk2yZNVdXmSTNent1skwAh6GDDaloNWVb2iql71wnaSP07yUJK7k9w07XZTki9ut0iAedPDgJ2wnY8O9yb5QlW98Dz/1N3/WlVfT3JXVb0nyfeTvHP7ZQLMnR4GDLfloNXd303ye+cY/58k12+nKIDR9DBgJ/hleACAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQdYNWlV1e1WdrqqHZsYuq6p7q+o70/Wl03hV1ceq6nhVPVhV14wsHmA9ehiwSBtZ0fpkkreeNXZLkiPdfSDJkel2krwtyYHpcjjJrfMpE2DLPhk9DFiQdYNWd38lyVNnDR9Kcse0fUeSt8+Mf6rXfDXJa6rq8nkVC7BZehiwSFs9R2tvd5+atn+YZO+0vS/JEzP7nZjGAHYTPQzYEds+Gb67O0lv9nFVdbiqjlbV0WdzZrtlAGyJHgaMtNWg9eQLy+nT9elp/GSS/TP7XTGN/ZLuvq27D3b3wUuyZ4tlAGyJHgbsiK0GrbuT3DRt35TkizPj756+uXNdkh/PLM8D7BZ6GLAjLl5vh6r6TJI3J3ltVZ1I8qEkf5Xkrqp6T5LvJ3nntPs9SW5IcjzJ/yb50wE1A2yYHgYs0rpBq7vfdZ67rj/Hvp3k5u0WBTAvehiwSH4ZHgBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGCQdYNWVd1eVaer6qGZsQ9X1cmqOjZdbpi57wNVdbyqHquqt4wqHGAj9DBgkTayovXJJG89x/jfdffV0+WeJKmqq5LcmOR3p8f8Q1VdNK9iAbbgk9HDgAVZN2h191eSPLXB5zuU5M7uPtPd30tyPMm126gPYFv0MGCRtnOO1nur6sFpWf7SaWxfkidm9jkxjf2SqjpcVUer6uizObONMgC2RA8Dhttq0Lo1yeuTXJ3kVJKPbPYJuvu27j7Y3QcvyZ4tlgGwJXoYsCO2FLS6+8nufr67f5bk4/nF0vrJJPtndr1iGgPYNfQwYKdsKWhV1eUzN9+R5IVv89yd5Maq2lNVVyY5kORr2ysRYL70MGCnXLzeDlX1mSRvTvLaqjqR5ENJ3lxVVyfpJI8n+bMk6e6Hq+quJI8keS7Jzd39/JjSAdanhwGLVN296Bry6rqs31jXL7oMYAfd1599oLsPLrqOedDD4MJyfx/J0/1UbWRfvwwPADCIoAUAMIigBQAwyLonwwOr7Us/OPbz7bf85tULrARg9VjRAgAYRNACfu5LPzj2ohUuALZH0IILmFAFMJZztOACJGAB7AwrWsCLOCEeYH4ELQCAQQQtAIBBBC24wLzU+Vk+NgSYL0ELAGAQQQsAYBBBCwBgEEELSOL8LIAR/GApXGAEKoCdY0ULAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYJB1g1ZV7a+qL1fVI1X1cFW9bxq/rKrurarvTNeXTuNVVR+rquNV9WBVXTN6EgDnon8Bi7aRFa3nkvx5d1+V5LokN1fVVUluSXKkuw8kOTLdTpK3JTkwXQ4nuXXuVQNsjP4FLNS6Qau7T3X3N6btZ5I8mmRfkkNJ7ph2uyPJ26ftQ0k+1Wu+muQ1VXX53CsHWIf+BSzaps7RqqrXJXlDkvuT7O3uU9NdP0yyd9rel+SJmYedmMYAFkb/AhZhw0Grql6Z5HNJ3t/dT8/e192dpDfzwlV1uKqOVtXRZ3NmMw8F2JR596/pOfUwYF0bClpVdUnWmtSnu/vz0/CTLyypT9enp/GTSfbPPPyKaexFuvu27j7Y3QcvyZ6t1g/wkkb0r0QPAzZmI986rCSfSPJod3905q67k9w0bd+U5Isz4++evr1zXZIfzyzRA+wY/QtYtIs3sM/vJ/mTJN+qqmPT2AeT/FWSu6rqPUm+n+Sd0333JLkhyfEk/5vkT+daMcDG6V/AQq0btLr735PUee6+/hz7d5Kbt1kXwLbpX8Ci+WV4AIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEEELQCAQQQtAIBBBC0AgEHWDVpVtb+qvlxVj1TVw1X1vmn8w1V1sqqOTZcbZh7zgao6XlWPVdVbRk4A4Hz0L2DRLt7APs8l+fPu/kZVvSrJA1V173Tf33X3387uXFVXJbkxye8m+c0k91XV73T38/MsHGAD9C9godZd0eruU939jWn7mSSPJtn3Eg85lOTO7j7T3d9LcjzJtfMoFmAz9C9g0TZ1jlZVvS7JG5LcPw29t6oerKrbq+rSaWxfkidmHnYi52hsVXW4qo5W1dFnc2bThQNsxjz71/R8ehiwrg0Hrap6ZZLPJXl/dz+d5NYkr09ydZJTST6ymRfu7tu6+2B3H7wkezbzUIBNmXf/SvQwYGM2FLSq6pKsNalPd/fnk6S7n+zu57v7Z0k+nl8sr59Msn/m4VdMYwA7Tv8CFmkj3zqsJJ9I8mh3f3Rm/PKZ3d6R5KFp++4kN1bVnqq6MsmBJF+bX8kAG6N/AYu2kW8d/n6SP0nyrao6No19MMm7qurqJJ3k8SR/liTd/XBV3ZXkkax94+dm39gBFkT/AhaqunvRNeTVdVm/sa5fdBnADrqvP/tAdx9cdB3zoIfBheX+PpKn+6nayL67ImhV1X8l+UmS/150Ldv02iz/HJLVmMcqzCFZjXmcbw6/1d2/vtPFjFBVzyR5bNF1zMEq/3tbNqswj1WYQ3LueWy4f+2KoJUkVXV02f93uwpzSFZjHqswh2Q15rEKc1jPqsxxFeaxCnNIVmMeqzCHZPvz8LcOAQAGEbQAAAbZTUHrtkUXMAerMIdkNeaxCnNIVmMeqzCH9azKHFdhHqswh2Q15rEKc0i2OY9dc44WAMCq2U0rWgAAK2XhQauq3lpVj1XV8aq6ZdH1bEZVPV5V36qqY1V1dBq7rKrurarvTNeXrvc8O2n6A7qnq+qhmbFz1lxrPjYdmwer6prFVf5i55nHh6vq5HQ8jlXVDTP3fWCax2NV9ZbFVP1iVbW/qr5cVY9U1cNV9b5pfKmOx0vMY6mOx1Ytaw9bxv6VrEYPW4X+laxGD9uR/tXdC7skuSjJfyb57SQvS/LNJFctsqZN1v94kteeNfY3SW6Ztm9J8teLrvOs+v4gyTVJHlqv5iQ3JPmXJJXkuiT3L7r+debx4SR/cY59r5r+be1JcuX0b+6iXTCHy5NcM22/Ksm3p1qX6ni8xDyW6nhsce5L28OWsX9NdS19D1uF/jXVtvQ9bCf616JXtK5Ncry7v9vdP01yZ5JDC65puw4luWPaviPJ2xdYyy/p7q8keeqs4fPVfCjJp3rNV5O8pl78N+IW5jzzOJ9DSe7s7jPd/b0kx/OLPyK8MN19qru/MW0/k+TRJPuyZMfjJeZxPrvyeGzRqvWwXd2/ktXoYavQv5LV6GE70b8WHbT2JXli5vaJvPQEd5tO8m9V9UBVHZ7G9nb3qWn7h0n2Lqa0TTlfzct4fN47LUnfPvOxx66fR1W9LskbktyfJT4eZ80jWdLjsQnLPJdV6V/JEr9nzrK075dV6GGj+teig9aye1N3X5PkbUlurqo/mL2z19YZl+prnctY84xbk7w+ydVJTiX5yGLL2ZiqemWSzyV5f3c/PXvfMh2Pc8xjKY/HBWTl+leyvHVnid8vq9DDRvavRQetk0n2z9y+YhpbCt19cro+neQLWVs+fPKFpdDp+vTiKtyw89W8VMenu5/s7ue7+2dJPp5fLOfu2nlU1SVZe3N/urs/Pw0v3fE41zyW8XhswdLOZYX6V7KE75mzLev7ZRV62Oj+teig9fUkB6rqyqp6WZIbk9y94Jo2pKpeUVWvemE7yR8neShr9d807XZTki8upsJNOV/Ndyd59/RNkeuS/HhmOXjXOeuz/ndk7Xgka/O4sar2VNWVSQ4k+dpO13e2qqokn0jyaHd/dOaupToe55vHsh2PLVrKHrZi/StZsvfMuSzj+2UVetiO9K/tnrG/3UvWvoXw7ayduf+Xi65nE3X/dta+efDNJA+/UHuSX0tyJMl3ktyX5LJF13pW3Z/J2jLos1n7bPk956s5a98M+fvp2HwrycFF17/OPP5xqvPB6c1w+cz+fznN47Ekb1t0/VNNb8rakvqDSY5NlxuW7Xi8xDyW6nhsY/5L18OWtX9NNS59D1uF/jXVtfQ9bCf6l1+GBwAYZNEfHQIArCxBCwBgEEELAGAQQQsAYBBBCwBgEEELAGAQQQsAYBBBCwBgkP8H65+pPbN+c/YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(20):\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.imshow(mask_batch[i].reshape(256,256))\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.imshow(predict_mask[i].reshape(256,256))\n",
    "    plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
